Fault Tolerant Relative Navigation using Inertial and Relative Sensors

Many emerging applications of space, ground, marine, and air vehicles require relative automated navigation with respect to other vehicles and objects. Disturbances in the environment may cause faults in relative navigation sensors. For sensors based on cameras or laser range finders, events as common as lighting changes, glint, or obstruction by debris could potentially cause spurious responses. Relative navigation is safety critical‐fault tolerance must be addressed. We propose a fault detection, identification, and recovery architecture using multiple moving horizon estimators, each for a separate hypothesis of the fault state of the system. The hypothesis with maximum empirical likelihood is selected. Detected and identified faults are reported to the main navigation filter, which may then discard the relative navigation sensor data, and instead temporarily rely on the inertial navigation system. The guidance system may also act on the identified fault state, taking actions to recover the system to a safe state. This logic is demonstrated in simulation for the automated rendezvous and docking (AR&D) of spacecraft‐a key technology for the near future demands of the space program. The simulation results demonstrate that faulty relative sensors may seriously aect the navigation solution. The proposed fault detection scheme has demonstrated an ability to identify faults in these sensors and take them oine before they disrupt navigation and lead to mission failure.

[1]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[2]  Stephen M. Rock,et al.  A Local-Area GPS Pseudolite-Based Navigation System for Mars Rovers , 2003, Auton. Robots.

[3]  Rahmat A. Shoureshi,et al.  Optimization based fault detection for nonlinear systems , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[4]  Silvio Simani,et al.  Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .

[5]  Gautam Biswas,et al.  Bayesian Fault Detection and Diagnosis in Dynamic Systems , 2000, AAAI/IAAI.

[6]  Jay H. Lee,et al.  A moving horizon‐based approach for least‐squares estimation , 1996 .

[7]  M. Tomizuka,et al.  Sensor fault detection in vehicle lateral control systems via switching Kalman filtering , 2005, Proceedings of the 2005, American Control Conference, 2005..

[8]  Bradley M. Bell,et al.  The Iterated Kalman Smoother as a Gauss-Newton Method , 1994, SIAM J. Optim..

[9]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[10]  Sebastian Thrun.  Winning the DARPA grand challenge , 2006 .

[11]  Huamin Jia,et al.  A Review of Multisensor Fusion Methodologies for Aircraft Navigation Systems , 2005 .

[12]  Wigbert Fehse,et al.  Automated Rendezvous and Docking of Spacecraft , 2003 .

[13]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[14]  Jonathan P. How,et al.  Real-Time Experimental Demonstration of Precise Decentralized Relative Navigation for Formation Flying Spacecraft , 2002 .

[15]  Stephen P. Boyd,et al.  Moving horizon filter for monotonic trends , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[16]  Rodney Teo.  Computing danger zones for provably safe closely spaced parallel approaches: Theory and experiment , 2003 .

[17]  Jason L. Speyer,et al.  Effective Estimation of Relative Positions in Orbit Using Differential Carrier-Phase GPS , 2004 .

[18]  Stephen Kemble,et al.  Automated Rendezvous and Docking of Spacecraft , 2007 .

[19]  M. Morari,et al.  Moving horizon estimation for hybrid systems , 2002, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[20]  Paul M. Frank,et al.  Model-Based Fault Diagnosis , 1992, Concise Encyclopedia of Modelling & Simulation.

[21]  J. H. Lala,et al.  Architectural principles for safety-critical real-time applications , 1994, Proc. IEEE.

[22]  P. Wroblewski,et al.  Design considerations for achieving MLS Category III requirements , 1989 .

[23]  Sebastian Thrun,et al.  Winning the DARPA Grand Challenge with an AI Robot , 2006, AAAI.

[24]  Michael E. Polites,et al.  An Assessment of the Technology of Automated Rendezvous and Capture in Space , 1998 .

[25]  Alexandre M. Bayen,et al.  Aircraft Autolander Safety Analysis Through Optimal Control-Based Reach Set Computation , 2007 .

[26]  Overview of the DART Mishap Investigation Results For Public Release , 2006 .